Introduction to COVID-19¶
Coronavirus is a family of viruses that can cause illness, which can vary from common cold and cough to sometimes more severe disease. Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV) were such severe cases with the world already has faced. SARS-CoV-2 (n-coronavirus) is the new virus of the coronavirus family, which first discovered in 2019, which has not been identified in humans before. It is a contiguous virus which started from Wuhan in December 2019. Which later declared as Pandemic by WHO due to high rate spreads throughout the world. Currently (on date 27 March 2020), this leads to a total of 24K+ Deaths across the globe, including 16K+ deaths alone in Europe. Pandemic is spreading all over the world; it becomes more important to understand about this spread. This NoteBook is an effort to analyze the cumulative data of confirmed, deaths, and recovered cases over time. In this notebook, the main focus is to analyze the spread trend of this virus all over the world.
Downloading and Installing Prerequisites¶
Download Pretrained Models from GitHub:¶
model_confirmed.h5
model_deaths.h5In [1]:
!pip install pycountry_convert
!pip install folium
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_deaths.h5
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_confirmed.h5
!wget https://raw.githubusercontent.com/tarunk04/COVID-19-CaseStudy-and-Predictions/master/models/model_usa_c.h5
Importing python libraries and datasets¶
In [2]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from matplotlib import ticker
import pycountry_convert as pc
import folium
!pip install branca
import branca
In [3]:
from datetime import datetime, timedelta, date
from scipy.interpolate import make_interp_spline, BSpline
!pip install plotly
import plotly.express as px
import json, requests
In [5]:
from keras.layers import Input, Dense, Activation, LeakyReLU
from keras import models
from keras.optimizers import RMSprop, Adam
In [6]:
df_confirmed = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
df_confirmed.head(5)
Out[6]:
In [10]:
df_deaths = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
print('Time series of Covid-19 Deaths: \n')
df_deaths.head(5)
Out[10]:
In [9]:
df_covid19 = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv")
print('Covid 19 Dataset: \n')
df_covid19.head()
Out[9]:
In [11]:
df_table = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_time.csv",parse_dates=['Last_Update'])
df_table.head()
Out[11]:
In [12]:
df_table = df_table[df_table["Country_Region"] != "Others"]
Finding country wise information
In [13]:
df_countries_cases = df_covid19.copy().drop(['Lat','Long_','Last_Update'],axis =1)
In [14]:
df_countries_cases.index = df_countries_cases["Country_Region"]
df_countries_cases= df_countries_cases.drop("Country_Region", axis=1)
df_countries_cases.head()
Out[14]:
Global cases reported till Date¶
Total number of Confirmed, Deaths, Recovered and Active cases across the globe
In [15]:
globalcases_df = pd.DataFrame(df_countries_cases.sum()).transpose().style.background_gradient(cmap='Wistia',axis=1)
globalcases_df
Out[15]:
Country Wise Reported Cases¶
Country Wise reported confirmed cases, recovered cases, deaths, active cases
In [16]:
df_countries_cases.sort_values('Confirmed', ascending= False).style.background_gradient(cmap='Wistia')
Out[16]:
Top 10 countries with Confirmed, Deaths, Recovered, Active cases¶
In [17]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Confirmed')["Confirmed"].index[-10:],df_countries_cases.sort_values('Confirmed')["Confirmed"].values[-10:],color="darkcyan")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 Countries (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 Countries (Confirmed Cases).png')
In [18]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Deaths')["Deaths"].index[-10:],df_countries_cases.sort_values('Deaths')["Deaths"].values[-10:],color="brown")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Death Cases",fontsize=18)
plt.title("Top 10 Countries (Death Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 Countries (Confirmed Cases).png')
In [19]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Recovered')["Recovered"].index[-10:],df_countries_cases.sort_values('Recovered')["Recovered"].values[-10:],color="green")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Recovered Cases",fontsize=18)
plt.title("Top 10 Countries (Recovered Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 Countries (Confirmed Cases).png')
In [20]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_countries_cases.sort_values('Active')["Active"].index[-10:],df_countries_cases.sort_values('Active')["Active"].values[-10:],color="orange")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Active Cases",fontsize=18)
plt.title("Top 10 Countries (Active Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 Countries (Confirmed Cases).png')
Correlation Analysis using Heatmap¶
Heat map on correlation of confirmed, recovered, deaths and active COVID-19 cases
In [21]:
df_countries_cases.corr().style.background_gradient(cmap='Reds')
Out[21]:
Visualization on world map using Folium¶
In [22]:
world_map = folium.Map(location=[10,0], tiles="cartodbpositron", zoom_start=2,max_zoom=6,min_zoom=2)
for i in range(0,len(df_confirmed)):
folium.Circle(
location=[df_confirmed.iloc[i]['Lat'], df_confirmed.iloc[i]['Long']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_confirmed.iloc[i]['Country/Region']+"</h5>"+
"<div style='text-align:center;'>"+str(np.nan_to_num(df_confirmed.iloc[i]['Province/State']))+"</div>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_confirmed.iloc[i,-1])+"</li>"+
"<li>Deaths: "+str(df_deaths.iloc[i,-1])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_deaths.iloc[i,-1]/(df_confirmed.iloc[i,-1]+1.00001)*100,2))+"</li>"+
"</ul>"
,
radius=(int((np.log(df_confirmed.iloc[i,-1]+1.00001)))+0.2)*50000,
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(world_map)
world_map
Out[22]:
Progression of COVID spreading worldwide¶
In [23]:
df_data = df_table.groupby(['Last_Update', 'Country_Region'])['Confirmed', 'Deaths'].max().reset_index()
df_data["Last_Update"] = pd.to_datetime( df_data["Last_Update"]).dt.strftime('%m/%d/%Y')
fig = px.scatter_geo(df_data, locations="Country_Region", locationmode='country names',
color=np.power(df_data["Confirmed"],0.3)-2 , size= np.power(df_data["Confirmed"]+1,0.3)-1, hover_name="Country_Region",
hover_data=["Confirmed"],
range_color= [0, max(np.power(df_data["Confirmed"],0.3))],
projection="natural earth", animation_frame="Last_Update",
color_continuous_scale=px.colors.sequential.Plasma,
title='COVID-19: Progression of spreading worldwide'
)
fig.update_coloraxes(colorscale="hot")
fig.update(layout_coloraxis_showscale=False)
fig.show()
Severity of COVID-19 across globe using Heatmap¶
In [34]:
temp_df = pd.DataFrame(df_countries_cases['Confirmed'])
temp_df = temp_df.reset_index()
fig = px.choropleth(temp_df, locations="Country_Region",
color=np.log10(temp_df.iloc[:,-1]), # lifeExp is a column of gapminder
hover_name="Country_Region", # column to add to hover information
hover_data=["Confirmed"],
color_continuous_scale=px.colors.sequential.Plasma,locationmode="country names")
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text="Confirmed Cases across globe")
fig.update_coloraxes(colorbar_title="Confirmed Cases across globe",colorscale="Reds")
# fig.to_image("Global Heat Map confirmed.png")
fig.show()
In [33]:
temp_df = pd.DataFrame(df_countries_cases['Deaths'])
temp_df = temp_df.reset_index()
fig = px.choropleth(temp_df, locations="Country_Region",
color=np.log10(temp_df.iloc[:,-1]), # lifeExp is a column of gapminder
hover_name="Country_Region", # column to add to hover information
hover_data=["Deaths"],
color_continuous_scale=px.colors.sequential.Plasma,locationmode="country names")
fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(title_text="Death Cases across globe")
fig.update_coloraxes(colorbar_title="Death Cases across globe",colorscale="Reds")
# fig.to_image("Global Heat Map confirmed.png")
fig.show()
COVID-19 : INDIA¶
Dataset:¶
This dataset is provided by API for COVID-19 stats in India, sourced from The Ministry of Health and Family Welfare and separately from unofficial sources https://api.rootnet.in/
In [36]:
df_covid19_india_test = pd.io.json.json_normalize(requests.get('https://api.rootnet.in/covid19-in/stats/testing/history').json()['data']).rename(columns = {"totalIndividualsTested":"covid_individualtested","totalPositiveCases":"corona_positives","totalSamplesTested":"totalsamplestested"})
df_covid19_india_test.head()
Out[36]:
In [37]:
df_covid19_india_test["pt_ratio"]= np.round(100*df_covid19_india_test["corona_positives"]/df_covid19_india_test["totalsamplestested"],2)
df_covid19_india_test["positive"] = df_covid19_india_test["corona_positives"].diff()
df_covid19_india_test["tests"] = df_covid19_india_test["totalsamplestested"].diff()
df_covid19_india_test["pt_ratio"]= np.round(100*df_covid19_india_test["positive"]/df_covid19_india_test["tests"],2)
In [42]:
df_covid19_india_test.tail()
Out[42]:
In [40]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.bar(df_covid19_india_test["day"],df_covid19_india_test["tests"].values[:],color="Green",label="Test (-ve)"+str())
plt.bar(df_covid19_india_test["day"],df_covid19_india_test["positive"].values[:],bottom=df_covid19_india_test["tests"].values[:]-df_covid19_india_test["positive"].values[:],color="Red",label="Test (+ve)")
plt.tick_params(size=5,labelsize = 13)
plt.tick_params(axis="x",size=5,labelsize = 13,labelrotation=90 )
plt.xlabel("Date",fontsize=18)
plt.ylabel("Test Results",fontsize=18)
plt.title("Tests in India",fontsize=20)
plt.grid(alpha=0.3)
plt.legend()
#plt.savefig(out+'Test_India.png')
Out[40]:
Total samples tested till April 1,2020¶
In [41]:
df_covid19_india_test["totalsamplestested"][-1:].values[0]
Out[41]:
COVID19 Cases in India¶
In [47]:
india_data_json = requests.get('https://api.rootnet.in/covid19-in/unofficial/covid19india.org/statewise').json()
df_india_cases = pd.io.json.json_normalize(india_data_json['data']['statewise'])
df_india_cases = df_india_cases.set_index("state")
df_india_cases.head()
Out[47]:
In [48]:
total = df_india_cases.sum()
total.name = "Total"
pd.DataFrame(total).transpose().style.background_gradient(cmap='Wistia',axis=1)
Out[48]:
In [49]:
total
Out[49]:
In [51]:
df_india_cases.style.background_gradient(cmap='Wistia')
Out[51]:
States with reported deaths¶
In [52]:
df_india_cases[df_india_cases["deaths"] > 0].style.background_gradient(cmap = 'Wistia')
Out[52]:
Top 10 states in India affected by COVID-19¶
States with Confirmed cases¶
In [64]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_india_cases.sort_values('confirmed')["confirmed"].index[-10:],df_india_cases.sort_values('confirmed')["confirmed"].values[-10:],color="orange")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 States in India (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 States_India (Confirmed Cases).png')
States with Death cases¶
In [65]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_india_cases.sort_values('deaths')["deaths"].index[-10:],df_india_cases.sort_values('deaths')["deaths"].values[-10:],color="brown")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Death Cases",fontsize=18)
plt.title("Top 10 States in India (Deaths Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 States_India (Confirmed Cases).png')
Correlation using Heatmap¶
In [67]:
df_india_cases.corr().style.background_gradient(cmap='Reds')
Out[67]:
In [78]:
# Adding states geolocation(Latitude,Longitude) data to India dataset
geolocations = {
"Kerala" : [10.8505,76.2711],
"Maharashtra" : [19.7515,75.7139],
"Karnataka": [15.3173,75.7139],
"Telangana": [18.1124,79.0193],
"Uttar Pradesh": [26.8467,80.9462],
"Rajasthan": [27.0238,74.2179],
"Gujarat":[22.2587,71.1924],
"Delhi" : [28.7041,77.1025],
"Punjab":[31.1471,75.3412],
"Tamil Nadu": [11.1271,78.6569],
"Haryana": [29.0588,76.0856],
"Madhya Pradesh":[22.9734,78.6569],
"Jammu and Kashmir":[33.7782,76.5762],
"Ladakh": [34.1526,77.5770],
"Andhra Pradesh":[15.9129,79.7400],
"West Bengal": [22.9868,87.8550],
"Bihar": [25.0961,85.3131],
"Chhattisgarh":[21.2787,81.8661],
"Chandigarh":[30.7333,76.7794],
"Uttarakhand":[30.0668,79.0193],
"Himachal Pradesh":[31.1048,77.1734],
"Goa": [15.2993,74.1240],
"Odisha":[20.9517,85.0985],
"Andaman and Nicobar Islands": [11.7401,92.6586],
"Puducherry":[11.9416,79.8083],
"Manipur":[24.6637,93.9063],
"Mizoram":[23.1645,92.9376],
"Assam":[26.2006,92.9376],
"Meghalaya":[25.4670,91.3662],
"Tripura":[23.9408,91.9882],
"Arunachal Pradesh":[28.2180,94.7278],
"Jharkhand" : [23.6102,85.2799],
"Nagaland": [26.1584,94.5624],
"Sikkim": [27.5330,88.5122],
"Dadra and Nagar Haveli":[20.1809,73.0169],
"Lakshadweep":[10.5667,72.6417],
"Daman and Diu":[20.4283,72.8397]
}
df_india_cases["Lat"] = ""
df_india_cases["Long"] = ""
for index in df_india_cases.index :
df_india_cases.loc[df_india_cases.index == index,"Lat"] = geolocations[index][0]
df_india_cases.loc[df_india_cases.index == index,"Long"] = geolocations[index][1]
Visualization using Folium map for statewise¶
In [90]:
# url = "https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States"
# state_json = requests.get(url).json()
india = folium.Map(location=[23,80], zoom_start=4,max_zoom=10, tiles='OpenStreetMap', attr= "INDIA" ,min_zoom=4,height=500,width="80%")
for i in range(0,len(df_india_cases[df_india_cases['confirmed']>0].index)):
folium.Circle(
location=[df_india_cases.iloc[i]['Lat'], df_india_cases.iloc[i]['Long']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_india_cases.iloc[i].name+"</h5>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_india_cases.iloc[i]['confirmed'])+"</li>"+
"<li>Active: "+str(df_india_cases.iloc[i]['active'])+"</li>"+
"<li>Recovered: "+str(df_india_cases.iloc[i]['recovered'])+"</li>"+
"<li>Deaths: "+str(df_india_cases.iloc[i]['deaths'])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_india_cases.iloc[i]['deaths']/(df_india_cases.iloc[i]['confirmed']+1)*100,2))+"</li>"+
"</ul>"
,
radius=(int(np.log2(df_india_cases.iloc[i]['confirmed']+1)))*15000,
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(india)
india
Out[90]:
COVID-19 US 
Load US dataset¶
In [92]:
df_temp = pd.read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/03-31-2020.csv")
df_usa_cases = df_temp.loc[df_temp["Country_Region"]== "US"]
df_usa_cases = df_usa_cases.rename(columns={"Admin2":"County"})
df_usa_cases.head()
Out[92]:
In [93]:
total = df_usa_cases.sum()
total
Out[93]:
In [94]:
total.name = "Total"
pd.DataFrame(total).transpose().loc[:,["Confirmed","Deaths"]].style.background_gradient(cmap='Wistia',axis=1)
Out[94]:
In [95]:
df_usa_cases.loc[:,["Confirmed","Deaths","Province_State"]].groupby(["Province_State"]).sum().sort_values("Confirmed",ascending=False).style.background_gradient(cmap='Wistia')
Out[95]:
Top 15 affected US Cities¶
In [96]:
df_usa_cases.loc[:,["Province_State","Confirmed","Deaths","County"]].sort_values("Confirmed",ascending=False).set_index("County")[:15].style.background_gradient(cmap='Wistia')
Out[96]:
Most affected states in US with COVID cases¶
Confirm cases¶
In [97]:
f = plt.figure(figsize=(10,5))
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_usa_cases.groupby(["Province_State"]).sum().sort_values('Confirmed')["Confirmed"].index[-10:],df_usa_cases.groupby(["Province_State"]).sum().sort_values('Confirmed')["Confirmed"].values[-10:],color="orange")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Confirmed Cases",fontsize=18)
plt.title("Top 10 States in US: (Confirmed Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 States in US (Confirmed Cases).png')
Death cases¶
In [98]:
f.add_subplot(111)
plt.axes(axisbelow=True)
plt.barh(df_usa_cases.groupby(["Province_State"]).sum().sort_values('Deaths')["Deaths"].index[-10:],df_usa_cases.groupby(["Province_State"]).sum().sort_values('Deaths')["Deaths"].values[-10:],color="brown")
plt.tick_params(size=5,labelsize = 13)
plt.xlabel("Death Cases",fontsize=18)
plt.title("Top 10 States in US: (Death Cases)",fontsize=20)
plt.grid(alpha=0.3)
#plt.savefig(out+'Top 10 States in US (Death Cases).png')
In [99]:
# url = "https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States"
# state_json = requests.get(url).json()
df_usa_cases = df_usa_cases.replace(np.nan, 0, regex=True)
usa = folium.Map(location=[37, -102], zoom_start=4,max_zoom=8,min_zoom=4)
for i in np.int32(np.asarray(df_usa_cases[df_usa_cases['Confirmed'] > 0].index)):
folium.Circle(
location=[df_usa_cases.loc[i]['Lat'], df_usa_cases.loc[i]['Long_']],
tooltip = "<h5 style='text-align:center;font-weight: bold'>"+df_usa_cases.loc[i]['Province_State']+"</h5>"+
"<div style='text-align:center;'>"+str(np.nan_to_num(df_usa_cases.loc[i]['County']))+"</div>"+
"<hr style='margin:10px;'>"+
"<ul style='color: #444;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
"<li>Confirmed: "+str(df_usa_cases.loc[i]['Confirmed'])+"</li>"+
"<li>Active: "+str(df_usa_cases.loc[i]['Active'])+"</li>"+
"<li>Recovered: "+str(df_usa_cases.loc[i]['Recovered'])+"</li>"+
"<li>Deaths: "+str(df_usa_cases.loc[i]['Deaths'])+"</li>"+
"<li>Mortality Rate: "+str(np.round(df_usa_cases.loc[i]['Deaths']/(df_usa_cases.loc[i]['Confirmed']+1)*100,2))+"</li>"+
"</ul>"
,
radius=(int(np.log2(df_usa_cases.loc[i]['Confirmed']+1)))*6000,
color='#ff6600',
fill_color='#ff8533',
fill=True).add_to(usa)
usa
Out[99]:
In [103]:
data_y = np.log10(np.asarray(df_confirmed.sum()[5:]).astype("float32"))
data_x = np.arange(1,len(data_y)+1)
Graph for COVID-19 cases all over the world¶
In [135]:
def plot_params(ax,axis_label= None, plt_title = None,label_size=15, axis_fsize = 15, title_fsize = 20, scale = 'linear' ):
# Tick-Parameters
ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.tick_params(which='both', width=1,labelsize=label_size)
ax.tick_params(which='major', length=6)
ax.tick_params(which='minor', length=3, color='0.8')
# Grid
plt.grid(lw = 1, ls = '-', c = "0.7", which = 'major')
plt.grid(lw = 1, ls = '-', c = "0.9", which = 'minor')
# Plot Title
plt.title( plt_title,{'fontsize':title_fsize})
# Yaxis sacle
plt.yscale(scale)
plt.minorticks_on()
# Plot Axes Labels
xl = plt.xlabel(axis_label[0],fontsize = axis_fsize)
yl = plt.ylabel(axis_label[1],fontsize = axis_fsize)
In [139]:
def visualize_covid_cases(confirmed, deaths, continent=None , country = None , state = None, period = None, figure = None, scale = "linear"):
x = 0
if figure == None:
f = plt.figure(figsize=(10,10))
# Sub plot
ax = f.add_subplot(111)
else :
f = figure[0]
# Sub plot
ax = f.add_subplot(figure[1],figure[2],figure[3])
plt.tight_layout(pad=10, w_pad=5, h_pad=5)
stats = [confirmed, deaths]
label = ["Confirmed", "Deaths"]
if continent != None:
params = ["continent",continent]
elif country != None:
params = ["country",country]
else:
params = ["All", "All"]
color = ["darkcyan","crimson"]
marker_style = dict(linewidth=3, linestyle='-', marker='o',markersize=4, markerfacecolor='#ffffff')
for i,stat in enumerate(stats):
if params[1] == "All" :
cases = np.sum(np.asarray(stat.iloc[:,5:]),axis = 0)[x:]
else :
cases = np.sum(np.asarray(stat[stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
date = np.arange(1,cases.shape[0]+1)[x:]
plt.plot(date,cases,label = label[i]+" (Total : "+str(cases[-1])+")",color=color[i],**marker_style)
if params[1] == "All" :
Total_confirmed = np.sum(np.asarray(stats[0].iloc[:,5:]),axis = 0)[x:]
Total_deaths = np.sum(np.asarray(stats[1].iloc[:,5:]),axis = 0)[x:]
else :
Total_confirmed = np.sum(np.asarray(stats[0][stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
Total_deaths = np.sum(np.asarray(stats[1][stat[params[0]] == params[1]].iloc[:,5:]),axis = 0)[x:]
text = "From "+stats[0].columns[5]+" to "+stats[0].columns[-1]+"\n"
text += "Mortality rate : "+ str(int(Total_deaths[-1]/(Total_confirmed[-1])*10000)/100)+"\n"
text += "Last 5 Days:\n"
text += "Confirmed : " + str(Total_confirmed[-1] - Total_confirmed[-6])+"\n"
text += "Deaths : " + str(Total_deaths[-1] - Total_deaths[-6])+"\n"
text += "Last 24 Hours:\n"
text += "Confirmed : " + str(Total_confirmed[-1] - Total_confirmed[-2])+"\n"
text += "Deaths : " + str(Total_deaths[-1] - Total_deaths[-2])+"\n"
plt.text(0.02, 0.78, text, fontsize=15, horizontalalignment='left', verticalalignment='top', transform=ax.transAxes,bbox=dict(facecolor='white', alpha=0.4))
# Plot Axes Labels
axis_label = ["Days ("+df_confirmed.columns[5]+" - "+df_confirmed.columns[-1]+")","No of Cases"]
# Plot Parameters
plot_params(ax,axis_label,scale = scale)
# Plot Title
if params[1] == "All" :
plt.title("COVID-19 Cases World",{'fontsize':25})
else:
plt.title("COVID-19 Cases for "+params[1] ,{'fontsize':25})
# Legend Location
l = plt.legend(loc= "best",fontsize = 15)
if figure == None:
plt.show()
In [137]:
cols = 1
rows = 1
f = plt.figure(figsize=(10,10*rows))
visualize_covid_cases(df_confirmed, df_deaths,continent = "All",figure = [f,rows,cols, 1])
#plt.savefig(out+'COIVD-19-World.png')
plt.show()
Loading Pre trained Model (model_confirmed.h5)¶
In [101]:
model = models.load_model("model_confirmed.h5")
model.summary()
In [141]:
model2 = models.load_model("model_deaths.h5")
model2.summary()
Predictions for COVID-19 cases¶
1. Prediction cases for COVID-19 globally¶
Using Moving Average and Exponential Weighted Moving Average models¶
In [149]:
def cal_moving_avg(input_df, window=2):
"""Calculates Moving avg using the window size."""
_df = input_df.rolling(window, axis=1).median()['4/1/20']
return _df
def cal_ewma(input_df, comm=0.3):
"""Calculates the exp weighted moving average using the window size."""
_df = input_df.ewm(com=comm).mean()['4/1/20']
return _df
In [151]:
all_pred_df = pd.DataFrame()
all_pred_df_ew = pd.DataFrame()
for df in [df_confirmed, df_deaths]:
_df = cal_moving_avg(df, window=2)
ew_df = cal_ewma(df, comm=0.4)
all_pred_df = pd.concat([all_pred_df, _df], axis=1)
all_pred_df_ew = pd.concat([all_pred_df_ew, ew_df], axis=1)
all_pred_df.columns =['Confirmed', 'Deaths']
all_pred_df_ew.columns =['Confirmed', 'Deaths']
In [173]:
print('\n')
print('COVID-19 cases worldwide till 1/4/2020 using Moving Average:')
all_pred_df
Out[173]:
In [174]:
print('\n')
print('COVID-19 cases worldwide till 1/4/2020 using EWMA:')
all_pred_df_ew
Out[174]:
2. Prediction cases for COVID-19 in India¶
In [176]:
ind_conf = df_confirmed[df_confirmed['Country/Region'] == 'India']
ind_conf
Out[176]:
In [177]:
ind_deaths = df_deaths[df_deaths['Country/Region'] == 'India']
ind_deaths
Out[177]:
Use Moving Average and EWMA models¶¶
In [175]:
def cal_moving_avg(input_df, window=2):
"""Calculates Moving avg using the window size."""
_df = input_df.rolling(window, axis=1).median()['4/1/20']
return _df
def cal_ewma(input_df, comm=0.3):
"""Calculates the exp weighted moving average using the window size."""
_df = input_df.ewm(com=comm).mean()['4/1/20']
return _df
In [179]:
ind_all_pred_df = pd.DataFrame()
ind_all_pred_df_ew = pd.DataFrame()
for df in [ind_conf, ind_deaths]:
_df = cal_moving_avg(df, window=2)
ew_df = cal_ewma(df, comm=0.4)
ind_all_pred_df = pd.concat([ind_all_pred_df, _df], axis=1)
ind_all_pred_df_ew = pd.concat([ind_all_pred_df_ew, ew_df], axis=1)
ind_all_pred_df.columns =['Confirmed', 'Deaths']
ind_all_pred_df_ew.columns =['Confirmed', 'Deaths']
In [180]:
print('\n')
print('COVID-19 cases in India till 1/4/2020 using MA:')
ind_all_pred_df
Out[180]:
In [181]:
print('\n')
print('COVID-19 cases in US till 1/4/2020 using EWMA:')
ind_all_pred_df_ew
Out[181]:
3. Prediction cases for COVID-19 in US¶
In [168]:
us_conf = df_confirmed[df_confirmed['Country/Region'] == 'US']
us_conf
Out[168]:
In [169]:
us_deaths = df_deaths[df_deaths['Country/Region'] == 'US']
us_deaths
Out[169]:
Use Moving Average and EWMA models¶
In [163]:
def cal_moving_avg(input_df, window=2):
"""Calculates Moving avg using the window size."""
_df = input_df.rolling(window, axis=1).median()['4/1/20']
return _df
def cal_ewma(input_df, comm=0.3):
"""Calculates the exp wighted moving average using the window size."""
_df = input_df.ewm(com=comm).mean()['4/1/20']
return _df
In [170]:
us_all_pred_df = pd.DataFrame()
us_all_pred_df_ew = pd.DataFrame()
for df in [us_conf, us_deaths]:
_df = cal_moving_avg(df, window=2)
ew_df = cal_ewma(df, comm=0.4)
us_all_pred_df = pd.concat([us_all_pred_df, _df], axis=1)
us_all_pred_df_ew = pd.concat([us_all_pred_df_ew, ew_df], axis=1)
us_all_pred_df.columns =['Confirmed', 'Deaths']
us_all_pred_df_ew.columns =['Confirmed', 'Deaths']
In [171]:
print('\n')
print('COVID-19 cases in US till 1/4/2020 using MA:')
us_all_pred_df
Out[171]:
In [172]:
print('\n')
print('COVID-19 cases in US till 1/4/2020 using EWMA:')
us_all_pred_df_ew
Out[172]:
Save the results into excel file¶
In [189]:
!pip install openpyxl
all_pred_df.to_excel("COVID19_Global_Cases.xlsx", index = False)
In [ ]: